System and method for biasing search results based on topic familiarity

ABSTRACT

A familiarity level classifier comprises a stopwords engine for conducting a stopwords analysis of stopwords, e.g., introductory level stopwords and advanced level stopwords, in a document, e.g., a website; and a familiarity level classifier module for generating a document familiarity level based on the stopwords analysis. The classifier may be in an indexing module, a search engine, a user computer, or elsewhere in a computer network. The classifier may also include a reading level engine for conducting a reading level analysis of the document, and wherein the familiarity level classifier module is configured to generate the familiarity level also based on the reading level analysis. The classifier may also include a document features engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the document familiarity level also based on the feature analysis.

PRIORITY CLAIM

This application claims benefit of and hereby incorporates by reference provisional patent application Ser. No. 60/662,307, entitled “Biasing Web Search Results for Topic Familiarity,” filed on Mar. 16, 2005, by inventors Jones et al.

TECHNICAL FIELD

This invention relates generally to document classifiers, and more particularly provides a system and method for classifying documents based on familiarity levels, e.g., biasing search results based on user familiarity with a topic.

BACKGROUND

The Internet has become a valuable resource of information on a variety of topics. Many websites are directed to teaching users general information about certain topics. Other websites are directed to providing users with cutting-edge information, so that experts can stay abreast of trends, new developments, research possibilities, etc.

When a user wishes to find information on the Internet about a particular topic, the user often directs his or her browser to a search engine and enters a query related to the topic. In response, the search engine applies a relevance function to identify the most relevant websites, and presents the results in an order corresponding to relevance scores. While the website results may relate to the topic, users still have to browse through the websites in an attempt to find those websites with the information on the topic at the particular level of detail desired, especially since different users have varying levels of sophistication. In other words, depending on a user's familiarity with a topic, the user may be more interested in receiving either introductory or advanced documents. A student searching for help with a linear algebra problem requires a different set of documents than a professor of mathematics interested in studying the latest in the field.

Empirically, it has been noted that a typical web search engine, e.g., the Yahoo! search engine, returns a mix of introductory and advanced documents in response to a query. Further, in response to the same query, the search engine will return the same website results to a novice and to an expert, failing to address the backgrounds and requirements of the two users. Currently, there has been no means for a user to inform the search engine of the amount of background knowledge the user has on a topic, so that the search engine can return only those documents appropriate to the user's level of expertise or so that the search engine can group documents according to introductory/advanced levels. Adding trigger words (e.g., “primer,” “introduction,” “information,” “definition,” “characteristic,” “summary,” etc.) to a query to suggest a user has only an introductory level of familiarity with a topic has been found statistically insignificant. Adding trigger words to a query to suggest a user has an advanced level of familiarity with a topic is more difficult.

A system and method are needed that enable automatic classification of documents based on user familiarity with a topic.

SUMMARY

In one embodiment, the present invention provides a familiarity level classifier that applies a familiarity level classification function which is topic-independent, query-independent and user-independent. The familiarity level classifier may determine the familiarity level of a document by analyzing three attributes, namely, (1) the distribution of stopwords in the text, (2) document reading level, and (3) document features such as average line-length. By classifying documents of a document pool, the familiarity level classifier enables ranking the document in the document pool based on familiarity level. Based on the familiarity levels, the documents can be organized so that introductory or advanced documents can be placed higher on the ranked list, so that the documents can be grouped based on their level of familiarity, so that select documents can be forwarded to an end-user based on familiarity level, etc.

In another embodiment, the present invention provides a method, comprising receiving a document; conducting a stopwords analysis of stopwords in the document; and generating a document familiarity level based on the stopwords analysis. The document may include a website. The document may be received at index time or in response to a query. The stopwords analysis may include conducting a stopwords analysis of introductory level stopwords and advanced level stopwords. The method may also include generating a stopwords score based on the stopwords analysis, the stopwords score including a binary value, a sliding scale value, or a descriptor from a set of descriptors. The method may also include conducting a reading level analysis and/or a feature analysis of the document, wherein the document familiarity level is generated based on the reading level analysis and/or feature analysis. The document familiarity level may be generated using a familiarity level classification function that incorporates weighting coefficients for each of the stopwords analysis, the reading level analysis and the feature analysis.

In another embodiment, the present invention provides a familiarity level classifier, comprising a stopwords engine for conducting a stopwords analysis of stopwords in a document; and a familiarity level classifier module for generating a document familiarity level based on the stopwords analysis. The document may include a website. The classifier may be in an indexing module, in a search engine, in a user computer, or elsewhere in the network. The stopwords engine may be configured to conduct a stopwords analysis of introductory level stopwords and advanced level stopwords. The stopwords engine may be configured to generate a stopwords score based on the stopwords analysis, wherein the stopwords score includes a binary value, a sliding scale value, or a descriptor from a set of descriptors. The classifier may also include a reading level engine for conducting a reading level analysis of the document and/or a document features engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the familiarity level also based on the reading level analysis and/or feature analysis. The familiarity level classifier module may be configured to implement a familiarity level classification function that incorporates weighting coefficients for each of the stopwords analysis, the reading level analysis and the feature analysis.

In yet another embodiment, the present invention provides a system, comprising means for receiving a document; means for conducting a stopwords analysis of stopwords in the document; and means for generating a document familiarity level based on the stopwords analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a familiarity level classification system, in accordance with an embodiment of the present invention.

FIG. 2 is a block diagram illustrating a familiarity level classification system in a first search engine environment, in accordance with an embodiment of the present invention.

FIG. 3 is a block diagram illustrating a familiarity level classification system in a second search engine environment, in accordance with an embodiment of the present invention.

FIG. 4 is a block diagram illustrating details of a familiarity level classifier of FIG. 1 or FIG. 2, in accordance with an embodiment of the present invention.

FIG. 5 is a block diagram illustrating details of a computer system, in accordance with an embodiment of the present invention.

FIG. 6 is a flowchart illustrating details of a method of generating and indexing familiarity levels of documents, in accordance with an embodiment of the present invention.

FIG. 7 is a flowchart illustrating details of a method of biasing search results based on familiarity levels, in accordance with a first embodiment of the present invention.

FIG. 8 is a flowchart illustrating details of a method of biasing search results based on familiarity levels, in accordance with a second embodiment of the present invention.

DETAILED DESCRIPTION

The following description is provided to enable any person skilled in the art to make and use the invention, and is provided in the context of a particular application and its requirements. Various modifications to the embodiments are possible to those skilled in the art, and the generic principles defined herein may be applied to these and other embodiments and applications without departing from the spirit and scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles, features and teachings disclosed herein.

FIG. 1 is a block diagram illustrating a familiarity level classification system 100, in accordance with an embodiment of the present invention. The classification system 100 includes a familiarity level classifier 110 that reviews documents from a document pool 105, determines a level of familiarity for a user to understand the document, and appends familiarity level information to the documents to generate a classified document pool 115, the documents being classified based on document familiarity level. The document pool 105 may include a web search result set or a subset of the web search result set. The level of familiarity may be a binary value (e.g., introductory or advanced), a sliding scale value (e.g., a number between 1 and 10 such that a document labeled as 1 is most introductory and a document labeled 10 is most advanced), a descriptor selected from a set of descriptors (e.g., introductory, medium, advanced, expert), etc.

The familiarity level classifier 110 applies a familiarity level classification function that is topic-independent, query-independent and user-independent. In one embodiment, the familiarity level classifier 110 determines the familiarity level of a document by analyzing three attributes of a document, namely, (1) the distribution of stopwords in the text, (2) document reading level, and (3) document features such as average line-length. By classifying the documents of the document pool 105, the familiarity level classifier 110 enables ranking the document pool 105 based on familiarity level.

The familiarity level classification system 100 further includes a response engine 120, e.g., a search engine, web browser, or other module. The response engine 120 operates on the classified document pool 115, e.g., to organize the documents so that introductory documents are placed higher on the ranked list, to group the documents based on their level of familiarity, to forward documents to an end-user based on familiarity level, etc. Although the response engine 120 could retrieve documents from only the set corresponding to the user's preference, this runs the risk of returning less relevant documents in favor of documents at an appropriate familiarity level.

FIG. 2 illustrates a familiarity level classification system 200 in a search engine environment, in accordance with a first embodiment of the present invention. The familiarity level classification system 200 includes user computers 205 coupled via a computer network 210 to websites 215. A crawler 220 (sometimes referred to as a robot or spider) is coupled to the network 210. An indexing module 225 is coupled to the crawler 220 and to an index data store 230. A search engine 235 is coupled to the index data store 230 and to the network 210.

The crawler 220 is configured to autonomously and automatically browse the billions of pages of websites 215 on the network 210, e.g., following hyperlinks, conducting searches of various search engines, following URL paths, etc. The crawler 220 obtains the documents (e.g., pages, images, text files, etc.) from the websites 215, and forwards the documents to the indexing module 225. An example crawler 120 is described more completely in U.S. Pat. No. 5,974,455 issued to Louis M. Monier on Oct. 26, 1999, entitled “System and Method for Locating Pages on the World-Wide-Web.”

Generally, the indexing module 225 parses the documents of the websites 115 received from the crawler 120 for fundamental indexable elements, e.g., atomic pairs of words and locations, dates of publication, domain information, etc. The indexing module 225 sorts the information from the many websites 115, according to their attributes, e.g., website X has 200 instances of the word “dog,” and sends the words, locations, and feature information to the index data store 230. The indexing module 225 may organize the feature information to optimize search query evaluation, e.g., may sort the information according to words, according to locations, etc. An example indexing module 225 is described in U.S. Pat. No. 6,021,409 issued to Burrows, et al., on Feb. 1, 2000, entitled “Method For Parsing, Indexing And Searching World-Wide-Web Pages” (“the Burrows patent”).

The index data store 230 stores the words 245, locations (e.g., URLs 250) and attributes 255 in various formats, e.g., compressed, organized, sorted, grouped, etc. The information is preferably indexed for quick query access. An example index data store 230 is described in detail in the Burrows patent.

In operation, the search engine 235 receives queries from user computers 205, and uses the index data store 230 and a search engine module 265 that applies a relevance function 270 to determine the most relevant documents in response to the queries. In response to the query, the search engine module 265 returns a list of the most relevant websites 215 to the user computer 205. The search engine 135 may store the query, the response, and possibly user actions (clicks, time on each site, etc.) in a query log (not shown), for future analysis, use and/or relevance function development/modification.

As shown in this embodiment, the indexing module 225 further includes a familiarity level classifier 240 similar to the familiarity level classifier 110 of FIG. 1. The familiarity level classifier 240 receives documents from the crawler 220, reviews the reading level, stopwords and document features of each document to determine its familiarity level, and provides the familiarity level 260 to the index data store 230 for future use. The index data store 230 stores the familiarity level 260 with the words 245, locations 250 and attributes 255.

The search engine 235 further includes a familiarity module 235. The familiarity module 235 reviews the familiarity levels 260 of a subset of the result set of relevant documents, e.g., the top 10, 20 or 100 documents, and groups, sorts, culls, etc. the result subset in accordance with the user's familiarity. When used for re-ranking a subset of the search results, the familiarity module 275 may only increase precision at the top of the list. Information identifying the familiarity of the user may be provided in the query. In another embodiment, the familiarity module 275 may be on the user computer 205, so that the result set/subset can be grouped, sorted, culled, etc. without the search engine 235. In such case, the familiarity module 275 on the user computer 205 would need access to the familiarity levels 260 of the documents in the result set or subset to enabling re-organization. The familiarity levels 260 may be appended to the documents in the result set or subset. The user computer 205 can rank results from low-to-high or high-to-low on the topic familiarity scale, possibly through a button or slider on the search page.

FIG. 3 illustrates a familiarity level classification system 300 in a search engine environment, in accordance with a second embodiment of the present invention. Familiarity level classification system 300 is similar to the familiarity level classification system 200 in FIG. 2. Element numbers are maintained the same in FIGS. 2 and 3 for convenience, although the elements need not be identical.

The familiarity level classification system 300 includes user computers 205 coupled via a computer network 210 to websites 215. A crawler 220 is coupled to the network 210. An indexing module 305 is coupled to the crawler 220 and to an index data store 310. A search engine 315 is coupled to the index data store 310 and to the network 210.

In this embodiment, the indexing module 305 is similar to the indexing module 225 of FIG. 2, except one difference is the absence of the familiarity level classifier 240. The index data store 310 is similar to the index data store 230, except one difference is that the index data store 310 does not store familiarity levels associated with the documents. The search engine 315 is similar to the search engine 235, except one difference is the replacement of the familiarity module 275 with a familiarity level classifier 320. In other embodiments, the familiarity level classifier 320 may be located on any computer in the network system 300, e.g., on the user computer 205.

In operation, the familiarity level classifier 320 can determine familiarity levels during runtime, and can organize, cull, group, etc. the result set or a subset of the result set before transferring the information to the user computer 205.

FIG. 4 is a block diagram illustrating details of an embodiment of the familiarity level classifier 400, which is an instance of classifiers 110/240/320. One skilled in the art will recognize that each classifier 110/240/320 may have minor variations, e.g., for communication purposes (e.g., requesting familiarity level information, forwarding re-ranked documents, etc). The familiarity level classifier 400 includes a stopwords engine 405, introductory-level stopwords 410, advanced-level stopwords 415, a reading level engine 420, a document features engine 425, a familiarity level classifier module 430 and a familiarity level function 435.

To avoid confounding the familiarity level classifier 400, the familiarity level classifier 400 implements a familiarity level classification function 435 that is user-independent, query-independent and topic-independent. For example, without query independence, a document describing molecular biology might be labeled as “advanced” for the query of “biology” or as “introductory” for the query of “molecular biology,” on the assumption that web searchers issuing these two queries would have different levels of topic familiarity. Accordingly, the familiarity level classifier 400 defines an introductory web page as “a web page that does not presuppose background knowledge of the topic it is on, and to an extent introduces or defines key terms on the topic,” and an advanced web page as “a web page that assumes sufficient background knowledge of the topic it is on and familiarity with the key technical/important terms on the topic, and potentially builds on them.”

Stopwords generally are words which are so common that they are generally quite useless to index or use in search engines or other search indexes. Stopwords may include articles, adverbials or adpositions. In English, stopwords may include “a”, “of”, “the”, “it”, “you”, “and,” etc. The stopwords engine 405 reviews a document to determine the presence of introductory level stopwords 410 (e.g., so, enough, just, in, needs, help, each, away) and the presence of advanced level stopwords 415 (e.g., if, cause, while, way, through, which, us). In this embodiment, the presence of an introductory level stopword 410 in a document suggests introductory content. The presence of advanced level stopword 415 in a document suggests advanced content. In other words, the presence of certain stopwords may suggest a particular genre or writing style, such that the presence of introductory level stopwords 410 suggests informal or colloquial writing and the presence of advanced-level stopwords 415 suggests more formal or scientific writing. The stopwords engine 405 may also review documents to obtain stopword frequencies, and may possibly use the Rainbow Library to assist with determining stopword frequencies. The stopwords engine 405 generates a stopwords score that predicts a familiarity level, and forwards the stopwords score to the familiarity level classifier module 430. In some embodiments, the sign of the stopwords score indicates the class (i.e., introductory or advanced). The stopwords score may be a binary value, a sliding scale value, a descriptor, etc.

The reading level engine 420 interprets reading level of a document to estimate familiarity level. Example features used to determine reading level include the Fog measure, the Flesch measure, the Kincaid measure, number of characters, number of words, percentage of complex words, number of sentences, number of text lines, number of blank lines, number of paragraphs, number of syllables per word, the number words per sentence, etc. To obtain reading level scores, the reading level engine 420 may implement publicly available techniques. For example, the reading level engine 420 may generate reading level scores using Fog, Flesch and/or Kincaid equations, which are shown in Equations 1, 2 and 3. Fog=(words-per-sentence+% complex-words)*0.4  (1) Flesch=206.835−(1.015*words-per-sentence)−(84.6*syllables-per-word)  (2) Kincaid=(11.8*syllables-per-word)+(0.39*words-per-sentence)−15.59  (3)

The Fog index is designed to indicate a number of years of formal education required to read a document once and understand it. In one test, the introductory and advanced documents scored an average of 20 (unreadable) on the Fog index. This may be due in part to outlier documents (the standard deviations were extremely high), and may be due in part to a mismatch between these indices, the form of web documents, and the automated way reading level features such as syllables per word are calculated. Introductory documents did average slightly lower than advanced documents. However, the difference was less than the standard deviation.

The Flesch index rates documents on a 100 point scale, with higher scores indicating greater readability and 60-70 considered optimal. In one test using the Flesch scale, example documents averaged around 20, with introductory documents slightly higher (slightly more readable) than advanced documents. However, like the Fog measure, the difference was less than the standard deviation.

The Kincaid measure scores reading level in terms of U.S. grade-school level. In several tests, the Kincaid measure scored introductory documents with slightly better readability than advanced documents. However, again, the difference was less than the standard deviation.

The reading level engine 420 generates a reading level score commensurate with the readability of the document, and forwards the reading level score to the familiarity level classifier module 430. Although the Fog, Flesch and Kincaid measures in some tests measured small differences between introductory and advanced documents, the familiarity level classification module 430 may amplify the variation to create greater distinction. The reading level score may be a binary value, a sliding scale value, a descriptor, etc.

The document features engine 425 examines document features to generate a features score indicative of familiarity level. Example discriminatory document features are listed in the table below.

Document features Hypothesis 1. Avg. num. of words per line with Web pages with a lot of non-anchor text anchor text removed. are introductory 2. Avg. num. of anchor text words per line An advanced web page has more anchor with other text removed. text per line 3. Document length excluding anchor Longer documents are introductory text. 4. Anchor text count. An advanced web page has more anchor text 5. Fraction of non-anchor text in Lower the fraction, more introductory document. the document 6. Average word length (excluding anchor Advanced documents have higher text). average word length due to more complex vocabulary. 7. Fraction of term “the” in text excluding Low fraction implies an introductory anchor text. document. 8. Fraction of term “a” in text excluding High fraction implies an introductory anchor text. document. 9. Fraction of term “an” in text excluding High fraction implies an introductory anchor text. document. 10. Average of the top five highest TFs. Salient terms are repeated in introductory documents. 11. Similarity of WordNet expansion of top The last 90% of an introductory 10% of document with remaining 90% document describes the first 10%

The document features engine 425 generates a features score indicative of the familiarity level of the document, and forward the features score to the familiarity level classifier module 430. The features score may be a binary value, a sliding scale value, a descriptor, etc.

The familiarity level classifier module 430 implements a familiarity level classification function 435 that uses the stopwords score, the reading level score and the document features score from the stopwords engine 405, the reading level engine 420 and the document features engine 425, respectively, to predict whether a document is introductory or advanced or to generate a familiarity level score indicative of how introductory or how advanced the document is. In one embodiment, the familiarity level classification function 435 includes weighting coefficients for each of the scores from each of the attribute engines 405, 420 and 425. In one embodiment, the stopwords score measured by the stopwords engine 405 carries the greatest weight. Other embodiments may predict familiarity level based only on the stopwords score from stopwords engine 405, a blend of the scores from the stopwords engine 405 and from the reading level engine 420, a blend of the scores from the stopwords engine 425 and the document features engine 425, etc. In some embodiments, the sign of the familiarity level indicates the class (i.e., introductory or advanced) and the magnitude of the familiarity level indicates how introductory or how advanced the document is in its class.

To generate the coefficients for the familiarity level classification function 435, a training algorithm (not shown) that can handle nonlinearity and a mix of different feature types with different value ranges can be used. In experiments, random forests proved to be the most convenient choice in addressing nonlinearity and handling the mix of different (numeric) feature types. Although experiments showed that random forests was most effective, linear SVMs and committees of perceptrons came close. Briefly, a random forest is the sum of the scores of k decision trees, where each decision tree is trained on a bootstrap sample of the training fold. At each tree level, a random feature is chosen and the best single partitioning value for that feature (minimizing the entropy) is chosen to partition the data at that node. Partitioning is done until all instances at a node have the same label. No pruning is performed.

Features other than stopwords that were often ranked high by the training algorithm included several reading level features (e.g., syllables-per-word, percent-complex-words), which had positive coefficients indicative of introductory documents, and one non-textual feature (average-word-length), which had a negative coefficient indicative of advanced documents.

An example familiarity level classification function 435 for determining the familiarity level of a document may be based on weighting coefficients of various stopwords, reading levels (see, e.g., Flesch, Kincaid, Fog) and document features. In one example, familiarity level may be determined by determining the feature values for each of the features provided in the list below. For stopwords, the feature values may be the number of instances of the word in each document. For reading levels, the feature values may be the value generated by the reading level function (e.g., Flesch, Kincaid, Fog). For document features, the feature values may be the values computed by the particular feature, e.g., a count value. Each of the feature values may be normalized by dividing the value by the maximum value of any document in the document pool. For example, if the number of instances of the word “so” in a document is 5, and the highest number of instances of the word “so” in any document in the document pool is 20, then the feature value for the document with 5 instances may be normalized to 5/20 or 0.25. Then, after all feature values (normalized per feature) are determined, the feature values are normalized to sum to one (1). For example, if the sum total of all feature values (normalized per feature) for a document is equal to 500, then the feature value for the word “so” will be normalized to 0.25/500 or 0.0005. After the second normalization, the feature values are each multiplied by their corresponding weighting coefficient and summed to generate the familiarity level of the document. In this embodiment, please note that the familiarity level classification function incorporates the summing of a stopwords score, a reading level score and a document features score.

Example weighting coefficients, in order of decreasing magnitude, are provided below. Please note that the “null” features below have no impact on the function result.

order. name id weight std 1. “so” (178) 1.24343 0.431106 2. “com” (128) −1.05705 0.289472 3. “comes” (435) −0.937931 0.384345 4. “follows” (428) −0.86139 0.400758 5. “by” (122) 0.722223 0.342136 6. “saw” (414) 0.709742 0.237394 7. “needs” (162) 0.707299 0.408867 8. “happens” (317) 0.701351 0.489256 9. “seven” (255) 0.664212 0.301502 10. “anybody” (572) −0.653046 0.172501 11. “specified” (352) −0.614471 0.410961 12. “sorry” (351) 0.606622 0.532332 13. “inner” (480) −0.588414 0.335875 14. “hardly” (519) −0.58534 0.344683 15. “meanwhile” (566) −0.58507 0.229559 16. “tell” (356) −0.564996 0.216349 17. “no” (241) −0.561807 0.339088 18. “both” (224) 0.561701 0.380051 19. “therein” (472) 0.560571 0.343155 20. “somewhere” (482) −0.536037 0.370152 21. “tries” (407) −0.535146 0.260744 22. “never” (332) −0.529589 0.311039 23. “themselves” (388) −0.528066 0.358335 24. “after” (102) −0.523398 0.296162 25. “become” (221) 0.523245 0.311741 26. “awfully” (548) −0.518193 0.28899 27. “sure” (355) 0.510046 0.36398 28. “nearly” (439) 0.505725 0.430119 29. “described” (508) −0.500482 0.324546 30. “outside” (338) 0.499711 0.374115 31. “ask” (292) −0.496784 0.35343 32. “all” (103) −0.492021 0.307784 33. “Number of anchor text (24) −0.487982 0.44553 words” 34. “appreciate” (384) 0.483741 0.544943 35. “although” (218) 0.482131 0.197582 36. “am” (106) −0.471326 0.346097 37. “beforehand” (558) 0.467536 0.391393 38. “second” (251) 0.459795 0.475948 39. “whole” (410) −0.45777 0.411724 40. “some” (179) 0.454114 0.25612 41. “really” (425) −0.453692 0.411103 42. “he” (319) 0.449621 0.426538 43. “latter” (446) 0.44947 0.354468 44. “our” (174) 0.436091 0.504575 45. “mainly” (236) 0.435064 0.458571 46. “mean” (454) −0.424392 0.231936 47. “allows” (466) −0.423072 0.345928 48. “against” (217) −0.419724 0.342119 49. “next” (164) −0.416808 0.501927 50. “seemed” (505) −0.412869 0.118696 51. “and” (109) 0.409452 0.546573 52. “regards” (522) −0.405913 0.359614 53. “normally” (532) −0.405045 0.521084 54. “other” (173) 0.392038 0.573997 55. “are” (112) 0.391378 0.434759 56. “whose” (267) 0.391357 0.369373 57. “my” (160) −0.390088 0.489109 58. “took” (473) −0.388525 0.201245 59. “him” (321) 0.388102 0.545436 60. “exactly” (419) −0.382414 0.167543 61. “three” (362) −0.380798 0.458944 62. “away” (293) 0.379971 0.410157 63. “became” (400) 0.379678 0.299571 64. “definitely” (552) 0.378304 0.431662 65. “been” (295) −0.376818 0.250037 66. “indicates” (493) −0.370361 0.331995 67. “elsewhere” (423) −0.368189 0.438238 68. “changes” (127) 0.347643 0.531522 69. “indicated” (492) −0.346402 0.188101 70. “often” (168) 0.345518 0.538341 71. “becoming” (377) 0.34191 0.238597 72. “done” (445) 0.341699 0.232952 73. “inc” (150) −0.338969 0.189191 74. “off” (279) 0.337766 0.281252 75. “her” (320) 0.337515 0.37747 76. “known” (154) −0.334781 0.279718 77. “ok” (484) −0.333719 0.236024 78. “amongst” (530) −0.331957 0.402409 79. “herein” (512) 0.331478 0.39888 80. “his” (322) 0.327521 0.394415 81. “greetings” (550) −0.327496 0.528934 82. “having” (318) 0.326909 0.458229 83. “every” (135) −0.323404 0.542644 84. “thank” (394) 0.322361 0.511765 85. “be” (116) −0.320163 0.46727 86. “is” (151) 0.316869 0.285395 87. “specifying” (578) −0.316173 0.49072 88. “its” (232) −0.315598 0.314516 89. “even” (307) 0.315597 0.376144 90. “please” (175) 0.315093 0.39228 91. “cant” (560) 0.310144 0.3513 92. “anyway” (434) −0.305827 0.103736 93. “when” (373) 0.304248 0.192646 94. “furthermore” (518) −0.303678 0.510704 95. “everywhere” (389) 0.302527 0.249673 96. “need” (161) 0.300746 0.44153 97. “many” (329) 0.298778 0.335644 98. “of” (167) −0.297952 0.523917 99. “somehow” (504) 0.295584 0.391044 100. “say” (415) −0.295023 0.527816 101. “thoroughly” (448) −0.294641 0.422302 102. “indeed” (513) −0.290978 0.243169 103. “following” (273) 0.290613 0.441764 104. “on” (169) 0.290406 0.386638 105. “re” (343) −0.290143 0.37064 106. “said” (345) −0.289747 0.409366 107. “course” (401) 0.287374 0.498002 108. “others” (336) −0.286242 0.35875 109. “maybe” (457) −0.285102 0.369365 110. “five” (227) −0.284807 0.50727 111. “forth” (553) 0.282735 0.180007 112. “twice” (459) −0.282329 0.31022 113. “always” (105) −0.276553 0.545321 114. “either” (436) −0.275882 0.419157 115. “not” (165) 0.275102 0.515357 116. “moreover” (494) −0.274062 0.220721 117. “theres” (576) −0.274039 0.407446 118. “throughout” (450) 0.270674 0.401317 119. “until” (366) 0.270131 0.278463 120. “six” (432) −0.266928 0.359235 121. “via” (409) −0.265865 0.506434 122. “yes” (376) −0.265311 0.418642 123. “usually” (368) −0.264496 0.467665 124. “tends” (483) 0.264144 0.447287 125. “certain” (299) −0.263371 0.276628 126. “specify” (540) −0.261697 0.465085 127. “value” (369) −0.259846 0.499858 128. “someone” (349) −0.259643 0.544859 129. “example” (310) −0.259267 0.312794 130. “down” (304) −0.257943 0.340713 131. “under” (284) 0.256497 0.294323 132. “rather” (250) −0.2549 0.512803 133. “everybody” (486) −0.254342 0.264646 134. “towards” (477) −0.253655 0.233701 135. “since” (406) 0.253382 0.441383 136. “than” (183) 0.252773 0.294936 137. “seriously” (471) −0.252017 0.423986 138. “very” (370) −0.250992 0.428249 139. “nine” (382) −0.249685 0.527259 140. “serious” (254) −0.24929 0.269675 141. “oh” (333) −0.246688 0.400243 142. “less” (326) 0.245854 0.427233 143. “que” (503) −0.245541 0.505595 144. “clearly” (507) −0.244823 0.422129 145. “way” (203) 0.244054 0.413626 146. “obviously” (467) −0.243953 0.218924 147. “inward” (570) 0.243608 0.398216 148. “had” (142) 0.242249 0.531209 149. “they” (189) 0.241873 0.45531 150. “me” (237) −0.241594 0.419497 151. “each” (133) −0.241481 0.575311 152. “reasonably” (539) 0.241186 0.516469 153. “which” (265) −0.241054 0.277778 154. “willing” (375) −0.240897 0.46807 155. “nevertheless” (521) −0.240474 0.167977 156. “being” (118) −0.239722 0.532211 157. “corresponding” (464) 0.239325 0.426298 158. “toward” (195) 0.238815 0.529727 159. “vs” (544) 0.2375 0.520269 160. “okay” (559) −0.236503 0.530814 161. “mostly” (438) 0.235148 0.520604 162. “though” (361) 0.234496 0.269881 163. “overall” (447) 0.233255 0.514158 164. “seems” (455) 0.232768 0.518118 165. “same” (346) 0.232471 0.406473 166. “un” (408) 0.232242 0.399939 167. “go” (141) −0.232038 0.199567 168. “around” (220) 0.22827 0.402699 169. “new” (163) −0.228042 0.345242 170. “help” (144) 0.223995 0.557362 171. “num_paragraphs” (10) −0.223295 0.531434 172. “whenever” (526) −0.223013 0.309431 173. “besides” (517) 0.222454 0.353995 174. “whom” (266) 0.222216 0.513156 175. “you” (213) 0.222102 0.222011 176. “another” (110) −0.221903 0.551279 177. “last” (381) 0.220536 0.292302 178. “none” (378) −0.218266 0.381821 179. “Average word length (26) −0.216809 0.0946313 (excluding anchor text)” 180. “better” (222) 0.216357 0.565476 181. “between” (223) 0.215033 0.536771 182. “name” (239) −0.214785 0.113971 183. “num_sentences” (7) −0.213699 0.525355 184. “somebody” (506) −0.213007 0.39508 185. “still” (353) 0.212817 0.483191 186. “aside” (528) −0.212471 0.541426 187. “have” (143) −0.211956 0.4422 188. “own” (246) 0.211691 0.432187 189. “come” (300) −0.211569 0.468852 190. “she” (348) 0.210749 0.27491 191. “do” (131) −0.208934 0.521423 192. “actually” (461) 0.208769 0.340389 193. “apart” (501) −0.208336 0.505414 194. “necessary” (277) −0.205756 0.303911 195. “namely” (485) 0.205193 0.191561 196. “num_blank_lines” (9) 0.204416 0.514446 197. “came” (123) 0.202237 0.502158 198. “possible” (341) 0.201593 0.392265 199. “far” (403) −0.200058 0.515979 200. “appear” (488) −0.20005 0.228998 201. “much” (159) 0.19979 0.535769 202. “presumably” (564) −0.199735 0.385492 203. “sensible” (253) 0.199517 0.46209 204. “num_text_lines” (8) −0.197653 0.540066 205. “Fraction of “a” in text (28) −0.195773 0.405187 excluding anchor text.” 206. “best” (120) 0.195416 0.240924 207. “allow” (449) −0.195187 0.297532 208. “provides” (249) 0.194578 0.197966 209. “thereby” (524) −0.194325 0.32564 210. “their” (186) 0.193959 0.516115 211. “out” (244) 0.193585 0.450077 212. “nor” (278) 0.19338 0.476916 213. “becomes” (392) 0.191324 0.537059 214. “above” (285) −0.190465 0.453726 215. “ourselves” (487) −0.18975 0.316612 216. “us” (259) −0.189414 0.133077 217. “entirely” (510) −0.189307 0.295445 218. “num_chars” (4) −0.187557 0.554027 219. “considering” (497) 0.187052 0.41611 220. “looking” (390) 0.186531 0.398076 221. “quite” (342) 0.185848 0.573645 222. “right” (176) −0.185716 0.541834 223. “containing” (460) 0.185088 0.550611 224. “hopefully” (542) −0.183931 0.166234 225. “why” (474) 0.183165 0.253426 226. “or” (172) −0.181549 0.292794 227. “relatively” (481) 0.180936 0.503263 228. “seeming” (568) −0.179574 0.532842 229. “truly” (442) −0.179296 0.169198 230. “using” (201) 0.179022 0.562014 231. “wonder” (427) 0.178229 0.542657 232. “well” (204) 0.176554 0.5198 233. “beside” (475) 0.1758 0.252289 234. “that” (184) −0.17493 0.513296 235. “accordingly” (516) −0.17414 0.37064 236. “tried” (196) −0.172555 0.468604 237. “per” (340) 0.171356 0.150059 238. “ltd” (429) 0.17121 0.367239 239. “should” (177) 0.170232 0.258611 240. “contains” (225) −0.168513 0.553377 241. “appropriate” (291) 0.166172 0.443202 242. “Avg # of anchor text words (22) −0.165447 0.11338 per line of lynx op with remaining text removed” 243. “from” (138) 0.164271 0.494533 244. “soon” (180) 0.162772 0.132958 245. “Fraction of “the” in text (27) 0.162484 0.0964551 excluding anchor text.” 246. “just” (152) 0.162293 0.427166 247. “then” (357) −0.161939 0.441317 248. “believe” (296) −0.161696 0.43086 249. “who” (208) −0.161503 0.585473 250. “something” (391) −0.160978 0.536854 251. “whoever” (577) −0.159684 0.537254 252. “went” (537) 0.159485 0.412349 253. “up” (199) −0.159417 0.289725 254. “we” (264) −0.158827 0.393247 255. “secondly” (514) 0.158703 0.214262 256. “thereupon” (565) −0.158103 0.465658 257. “unlikely” (554) −0.158002 0.47006 258. “welcome” (380) −0.157692 0.287587 259. “yet” (411) −0.156383 0.172094 260. “later” (325) 0.154057 0.31847 261. “thanks” (441) −0.15343 0.367931 262. “ignored” (569) −0.153139 0.50582 263. “edu” (226) −0.150305 0.223696 264. “into” (230) −0.150267 0.456966 265. “hi” (275) 0.149331 0.598412 266. “this” (190) −0.149179 0.464186 267. “rd” (280) −0.148986 0.516617 268. “across” (216) 0.145137 0.520149 269. “along” (399) −0.144809 0.366637 270. “again” (287) −0.144794 0.380911 271. “unless” (365) 0.143547 0.487478 272. “once” (334) −0.143234 0.292598 273. “various” (468) 0.142347 0.400381 274. “gotten” (567) −0.142209 0.501292 275. “like” (155) 0.14039 0.312012 276. “nobody” (393) −0.13969 0.360666 277. “Avg # of words per line (21) 0.139324 0.0947199 of lynx op with anchor text removed.” 278. “what” (205) 0.139161 0.499963 279. “if” (147) −0.139066 0.574746 280. “in” (149) −0.137286 0.572394 281. “anything” (463) 0.136676 0.282932 282. “self” (252) −0.133916 0.31453 283. “able” (215) −0.133815 0.553676 284. “goes” (315) 0.132963 0.520018 285. “about” (101) 0.131516 0.128355 286. “available” (269) −0.131126 0.393303 287. “liked” (546) −0.131073 0.37674 288. “ex” (541) −0.12964 0.440525 289. “anyone” (219) 0.129208 0.541087 290. “saying” (462) 0.127421 0.561404 291. “therefore” (188) 0.126392 0.574498 292. “num_words” (5) −0.125608 0.563933 293. “were” (372) −0.125433 0.496121 294. “has” (229) −0.125419 0.40344 295. “before” (117) −0.125263 0.563085 296. “behind” (496) −0.124484 0.561325 297. “keep” (234) 0.12431 0.435024 298. “whereby” (561) −0.122902 0.475409 299. “without” (211) 0.122704 0.568703 300. “during” (305) 0.121573 0.473659 301. “ought” (533) 0.121511 0.503518 302. “for” (137) −0.120715 0.394562 303. “non” (242) 0.120179 0.0965076 304. “somewhat” (538) −0.119715 0.278949 305. “kept” (404) 0.11957 0.279296 306. “beyond” (297) 0.119061 0.544531 307. “within” (268) −0.118828 0.41215 308. “especially” (424) −0.117882 0.525971 309. “brief” (489) −0.117782 0.409383 310. “nothing” (420) 0.117002 0.412791 311. “over” (245) −0.116779 0.504738 312. “through” (192) 0.116557 0.570729 313. “consider” (130) 0.116399 0.255415 314. “perhaps” (499) −0.115961 0.358842 315. “more” (157) −0.115713 0.309937 316. “Document length (word (23) −0.11475 0.548326 count) without anchor text” 317. “regardless” (344) −0.1147 0.54494 318. “wish” (495) 0.113606 0.546991 319. “an” (108) 0.112989 0.543934 320. “keeps” (476) −0.111798 0.242899 321. “except” (271) 0.110467 0.578253 322. “near” (413) 0.110022 0.371633 323. “thru” (416) −0.109429 0.504315 324. “any” (111) 0.107028 0.452159 325. “almost” (398) 0.106641 0.309155 326. “few” (272) 0.106263 0.173808 327. “concerning” (129) 0.105738 0.545963 328. “little” (156) 0.105504 0.476988 329. “lately” (498) 0.105476 0.181527 330. “could” (417) −0.104647 0.114377 331. “certainly” (444) 0.103775 0.499626 332. “gets” (451) −0.102493 0.447535 333. “et” (502) 0.101197 0.503831 334. “yourself” (397) 0.0994363 0.311029 335. “wherein” (515) −0.0993186 0.585591 336. “together” (194) 0.0977063 0.373475 337. “indicate” (491) 0.0973426 0.466296 338. “at” (115) 0.0966459 0.579053 339. “given” (313) 0.0964377 0.389812 340. “asking” (114) 0.0959791 0.575355 341. “already” (289) −0.0948876 0.468117 342. “theirs” (543) −0.094668 0.512287 343. “myself” (551) −0.0943295 0.531432 344. “because” (294) −0.0938475 0.485537 345. “despite” (456) 0.0934967 0.457577 346. “used” (260) 0.0921309 0.496812 347. “several” (347) −0.0907439 0.484138 348. “1.0 - fraction of anchor text” (25) −0.09074 0.131918 349. “formerly” (556) 0.0905694 0.454271 350. “may” (276) −0.0873362 0.513008 351. “else” (306) −0.086794 0.527802 352. “himself” (453) 0.0867821 0.266228 353. “fifth” (478) −0.0864953 0.28114 354. “says” (421) 0.085355 0.546678 355. “enough” (134) 0.0853015 0.308536 356. “first” (136) −0.0850254 0.566978 357. “merely” (330) 0.0844566 0.528927 358. “hereby” (386) −0.0825733 0.43426 359. “the” (185) −0.0823531 0.548644 360. “novel” (430) −0.0810018 0.493381 361. “would” (212) 0.0802326 0.415364 362. “seem” (535) −0.0795525 0.455681 363. “flesch” (2) 0.0793815 0.464431 364. “particular” (339) −0.0793443 0.395399 365. “zero” (555) −0.0792081 0.311654 366. “did” (412) 0.0785752 0.485849 367. “one” (170) −0.0784201 0.556447 368. null (35) 0.078026 0.582182 369. “etc” (418) −0.0777686 0.232471 370. “thorough” (360) 0.0773932 0.393185 371. “thats” (549) −0.0770457 0.293841 372. “let” (385) −0.0765314 0.115686 373. “get” (139) 0.0750087 0.530255 374. “want” (202) −0.074861 0.560318 375. “eight” (469) 0.0747994 0.2295 376. “sub” (256) 0.0744016 0.535779 377. “yours” (383) 0.073955 0.4903 378. “think” (258) −0.0737303 0.426032 379. “instead” (324) −0.0733831 0.179677 380. “among” (107) −0.0733673 0.545769 381. “old” (243) 0.0723823 0.361828 382. “unfortunately” (536) −0.0721401 0.198608 383. “take” (181) −0.0717333 0.547438 384. “doing” (132) 0.0716102 0.562868 385. “according” (286) −0.0715544 0.576836 386. “cause” (125) −0.0704921 0.492878 387. “these” (358) 0.0700587 0.460573 388. “whether” (374) 0.0695506 0.453576 389. “with” (210) 0.0694474 0.466898 390. “thence” (573) −0.0678765 0.56831 391. “will” (209) 0.066577 0.516757 392. “was” (263) −0.0663541 0.574323 393. “placed” (379) −0.0660772 0.346278 394. “seen” (479) −0.0659723 0.505724 395. “upon” (367) 0.0657723 0.248498 396. “wants” (371) 0.0644256 0.202835 397. “probably” (500) 0.0634823 0.188288 398. “while” (207) −0.0613835 0.125429 399. null (46) 0.0612267 0.590843 400. “further” (312) 0.0606675 0.449438 401. “Fraction of “an” in text (29) −0.0593747 0.138265 excluding anchor text.” 402. null (20) 0.0593322 0.551894 403. “four” (228) −0.0591526 0.556366 404. “anywhere” (290) 0.0590882 0.450743 405. “wherever” (426) 0.0586491 0.32174 406. “alone” (288) −0.0584367 0.229981 407. “such” (354) 0.0582371 0.553083 408. null (34) 0.0580683 0.564831 409. “otherwise” (337) 0.0579097 0.556272 410. “can” (124) −0.0573672 0.566604 411. “uses” (262) 0.0573448 0.569534 412. “unto” (557) −0.0557843 0.29282 413. “look” (328) −0.0555572 0.554487 414. “viz” (525) −0.0554966 0.428656 415. “where” (206) −0.0553248 0.558178 416. “as” (113) −0.0550984 0.544366 417. “ie” (437) 0.0548309 0.344708 418. “co” (270) −0.054605 0.115667 419. “below” (119) −0.0533078 0.269191 420. “regarding” (405) 0.0526944 0.568752 421. “hence” (511) −0.0518125 0.371168 422. “your” (214) 0.0508094 0.338665 423. null (95) 0.0504889 0.57204 424. “immediate” (148) 0.0500414 0.411872 425. “plus” (248) 0.0500069 0.547414 426. “does” (303) −0.0496776 0.557271 427. “going” (316) 0.0494902 0.141309 428. “nowhere” (440) 0.0493186 0.148353 429. “how” (146) −0.048353 0.573056 430. null (55) 0.0482441 0.567683 431. null (33) 0.0469152 0.595646 432. “herself” (452) −0.0468346 0.365021 433. “to” (193) −0.0464596 0.53488 434. null (73) 0.0463794 0.573055 435. “whereas” (527) 0.046254 0.50354 436. null (80) 0.046035 0.575191 437. null (18) 0.0456669 0.587246 438. “different” (302) −0.0454226 0.414251 439. “syllables_per_word” (11) 0.0445158 0.0810561 440. “sometime” (575) −0.0439125 0.569956 441. “least” (387) 0.0437659 0.510981 442. “it” (231) 0.0426356 0.250492 443. “also” (104) −0.0422691 0.541659 444. “qv” (571) −0.0420383 0.394455 445. “shall” (422) −0.0420361 0.472767 446. “percent_complex_words” (6) −0.0416394 0.098915 447. null (64) 0.0413607 0.57636 448. “useful” (261) −0.0399627 0.25127 449. “eg” (562) −0.0395573 0.41768 450. “must” (238) −0.0384369 0.524003 451. null (39) 0.0377421 0.577618 452. null (79) 0.0373912 0.574735 453. “sup” (433) −0.037357 0.408118 454. “however” (323) −0.0373109 0.216392 455. null (16) 0.0362969 0.580413 456. “followed” (470) −0.0356895 0.183624 457. “ours” (545) 0.0355177 0.433293 458. null (78) 0.0349696 0.558135 459. “try” (197) 0.0346534 0.256942 460. “those” (191) 0.0344842 0.570277 461. “them” (187) −0.0343415 0.569149 462. null (41) 0.0338687 0.579237 463. “gives” (314) 0.0329499 0.252902 464. “respectively” (431) −0.0326653 0.395434 465. null (53) 0.0325171 0.551269 466. null (14) 0.032318 0.570473 467. “getting” (140) 0.0321962 0.485858 468. “third” (359) −0.0319038 0.475615 469. “knows” (235) −0.0311182 0.556308 470. null (37) 0.0306648 0.578682 471. null (87) 0.030473 0.557271 472. null (48) 0.0303094 0.583154 473. null (44) 0.0300287 0.575987 474. “see” (281) −0.0299924 0.510299 475. null (57) 0.0298343 0.567072 476. “everything” (309) 0.0296675 0.471965 477. “Average of the top five (30) 0.0293436 0.108904 highest web IDFs” 478. “sent” (282) 0.0293161 0.374278 479. null (60) 0.0291397 0.587952 480. “causes” (126) −0.029118 0.534274 481. null (77) 0.0288585 0.570216 482. “only” (171) −0.0285484 0.57056 483. “particularly” (247) 0.0274855 0.563178 484. “consequently” (529) −0.0270428 0.560391 485. “lest” (520) 0.026956 0.569501 486. “kincaid” (3) 0.0265465 0.527479 487. “itself” (233) −0.0264856 0.552301 488. “most” (158) −0.0261055 0.559712 489. “onto” (465) 0.0259714 0.262483 490. “thereafter” (523) 0.0259034 0.14007 491. “but” (121) 0.0246229 0.543958 492. “sometimes” (350) 0.0238818 0.387382 493. null (63) 0.0238779 0.579536 494. null (17) 0.0231518 0.58899 495. “whatever” (443) −0.0226753 0.582677 496. null (45) 0.0225388 0.545589 497. “associated” (395) −0.0219575 0.502391 498. “two” (198) 0.0218879 0.571762 499. null (90) 0.0217011 0.569658 500. “might” (331) −0.0209851 0.543155 501. “words_per_sentence” (12) 0.0209161 0.528976 502. null (85) 0.0208549 0.559955 503. null (62) 0.0207868 0.5834 504. null (50) 0.0206237 0.576653 505. null (82) 0.0203684 0.579892 506. null (13) 0.02016 0.564816 507. “former” (311) −0.0199755 0.565022 508. “there” (257) −0.0193889 0.536455 509. null (51) 0.0189772 0.590405 510. null (49) 0.0188936 0.601399 511. null (84) 0.0188629 0.558694 512. “looks” (531) −0.0186741 0.580641 513. null (56) 0.018664 0.582126 514. “insofar” (563) 0.0185199 0.327621 515. null (66) 0.0181481 0.577965 516. null (74) 0.0180852 0.563354 517. “everyone” (308) 0.0177242 0.518573 518. “taken” (182) −0.0175745 0.225554 519. “likely” (327) 0.017207 0.425293 520. “know” (153) 0.0170323 0.412988 521. null (40) 0.0169843 0.57053 522. “thus” (363) 0.0169428 0.539957 523. null (75) 0.0167014 0.559415 524. null (68) 0.0160537 0.583812 525. null (15) 0.0157574 0.5691 526. null (70) 0.0155226 0.562965 527. “currently” (402) 0.0153056 0.4084 528. “gone” (490) −0.0147289 0.537004 529. null (94) 0.0143283 0.577168 530. null (99) 0.0138578 0.575932 531. null (67) 0.0137805 0.582027 532. “cannot” (298) 0.013701 0.5472 533. null (92) 0.0131282 0.582806 534. null (76) 0.0129182 0.558516 535. null (89) 0.0128523 0.589602 536. null (61) 0.0122759 0.584549 537. “now” (166) 0.0121919 0.485761 538. “fog” (1) −0.0116886 0.506574 539. “nd” (240) −0.0114611 0.537238 540. null (59) 0.0112281 0.56056 541. null (42) 0.0108568 0.586807 542. “Average of the top five (31) 0.0106864 0.098148 highest TFs” 543. null (52) 0.0103249 0.575719 544. “ones” (335) −0.0102175 0.514625 545. “too” (364) 0.0101281 0.570721 546. null (96) 0.00980408 0.570074 547. “here” (145) −0.00953005 0.508984 548. null (71) 0.00893064 0.552502 549. null (58) 0.00831833 0.573421 550. null (98) 0.00826938 0.582983

The above example weighting coefficients lend themselves to a threshold level of zero, such that the magnitude of a familiarity level above zero identifies how introductory a document is and the magnitude of a familiarity level below zero identifies how advanced a document is. One skilled in the art will recognize that the threshold level can be modified as a tradeoff for more confidence in the introductory nature or the advanced nature of a document.

In one embodiment, the familiarity level classifier 240/320 may incorporate techniques from Harper et al's HARD 2004 work, which assumes that users unfamiliar with a topic prefer documents with representative terms and that users familiar with a topic prefer documents with discriminative terms. Such representative/discriminatory terms may be identified using a clarity measure. By interpreting the clarity measure in a particular way, the familiarity level classifier 240/320 may select representative and/or discriminative terms, which it can use to modify the query. In one embodiment, the familiarity level classifier 240/320 may apply this technique to obtain a search result, which can be classified using the familiarity level classification function 435.

FIG. 5 is a block diagram illustrating details of an example computer system 500, of which the familiarity level classifier 110, the response engine 120, the search engine 235/315, the crawler 220, the user computers 205, the websites 215, the indexing module 225/305, the index data store 230/310, etc. may be instances. Computer system 500 includes a processor 505, such as an Intel Pentium® microprocessor or a Motorola Power PC® microprocessor, coupled to a communications channel 510. The computer system 500 further includes an input device 515 such as a keyboard or mouse, an output device 520 such as a cathode ray tube display, a communications device 525, a data storage device 530 such as a magnetic disk, and memory 535 such as Random-Access Memory (RAM), each coupled to the communications channel 510. The communications interface 525 may be coupled to a network such as the wide-area network commonly referred to as the Internet. One skilled in the art will recognize that, although the data storage device 530 and memory 535 are illustrated as different units, the data storage device 530 and memory 535 can be parts of the same unit, distributed units, virtual memory, etc.

The data storage device 530 and/or memory 535 may store an operating system 540 such as the Microsoft Windows NT or Windows/95 Operating System (OS), the IBM OS/2 operating system, the MAC OS, or UNIX operating system and/or other programs 545. It will be appreciated that an embodiment may be implemented on platforms and operating systems other than those mentioned. An embodiment may be written using JAVA, C, and/or C++ language, or other programming languages, possibly using object oriented programming methodology.

One skilled in the art will recognize that the computer system 500 may also include additional information, such as network connections, additional memory, additional processors, LANs, input/output lines for transferring information across a hardware channel, the Internet or an intranet, etc. One skilled in the art will also recognize that the programs and data may be received by and stored in the system in alternative ways. For example, a computer-readable storage medium (CRSM) reader 550 such as a magnetic disk drive, hard disk drive, magneto-optical reader, CPU, etc. may be coupled to the communications bus 510 for reading a computer-readable storage medium (CRSM) 555 such as a magnetic disk, a hard disk, a magneto-optical disk, RAM, etc. Accordingly, the computer system 500 may receive programs and/or data via the CRSM reader 550. Further, it will be appreciated that the term “memory” herein is intended to cover all data storage media whether permanent or temporary.

FIG. 6 is a flowchart illustrating a method 600 of biasing web search results based on familiarity level, in accordance with an embodiment of the present invention. Method 600 begins in step 605 with the crawler 220 locating a document for indexing. The stopwords engine 405 in step 610 uses the introductory level stopwords 410 and the advanced level stopwords 415 to determine a stopwords score. In one example, the stopwords score is based on a function of the number of times particular stopwords are presented in the document. The reading level engine 420 in step 615 determines a reading level score of the document. In one example, the reading level score is based on a combination of Fog, Flesch and Kincaid equations, in addition to readability features such as the number of characters, number of words, percentage of complex words, number of sentences, number of text lines, number of blank lines, number of paragraphs, number of syllables per word, the number words per sentence, etc. The document features engine 425 in step 620 determines the document features score. In one example the document features score is based on document features such as the average number of words per line with anchor text removed, the average number of anchor text words per line with other text removed, the document length excluding anchor text, the anchor text count, the fraction of non-anchor text in document, the average word length (excluding anchor text), the fraction of term “the” in text excluding anchor text, the fraction of term “a” in text excluding anchor text, the fraction of term “an” in text excluding anchor text, the average of the top five highest TFs, and the similarity of WordNet expansion of top 10% of document with remaining 90%.

The familiarity level classifier module 430 in step 625 inputs the stopwords score, the reading level score and the document features score into a familiarity level classification function 435 to generate a familiarity level for the document. In one example, the stopwords score, reading level score and document features score are normalized values, whether each a defined as a binary value, a sliding scale value or a descriptor. The familiarity level classification function 435 may use weighting coefficients for each of the scores, such that each score need not be weighted equally. For example, the stopwords score may be weighted more heavily than the other scores. The classification function 435 may incorporate other scores such as a clarity score indicative of the clarity of the document. The indexing module 225 in step 630 indexes the familiarity level score in the index data store 230 for future use. Method 600 then ends.

FIG. 7 is a flowchart illustrating a method 700 of biasing search results based on the familiarity scores, in accordance with first embodiment of the present invention. Method 700 begins with the search engine 235 in step 705 receiving a search query from the user computer 205. The search engine 235 in step 710 also receives a familiarity level request from the user computer 205. The search engine module 265 of the search engine 235 in step 715 generates search results using the relevance function 270. In one example, the search engine module 265 does not use the familiarity levels to generate the search results. The familiarity module 275 on the search engine 235 in step 720 selects a subset of the search results. In one example, the familiarity module 275 in step 720 selects the top 10, 20, 50 or 100 documents of the search results as the subset. The familiarity module 275 in step 725 organizes the subset of the results using the familiarity levels and the familiarity level request. Then, the search engine 235 in step 730 forwards the results to the user computer 205. Method 700 then ends.

FIG. 8 is a flowchart illustrating a method 800 of biasing search results based on the familiarity scores, in accordance with a second embodiment of the present invention. Method 800 begins with the search engine 235 in step 805 receiving a search query from the user computer 205. The search engine module 265 of the search engine 235 in step 810 generates search results using the relevance function 270. In one example, the search engine module 265 does not use the familiarity levels to generate the search results. The search engine 235 in step 815 forwards the search results and familiarity levels for the documents or a portion of the documents to the user computer 205. A familiarity module in communication with a browser on the user computer 205 in step 820 receives a familiarity level request from the requestor. The familiarity module may be similar to familiarity module 275, may be on the user computer 205, may be integral with the browser on the user computer 205, or may be on another computer 205 in the network 210. The familiarity module in step 825 selects a subset of the search results. In one example, the familiarity module select the top 10, 20, 50 or 100 documents of the search results as the subset. The familiarity module in step 830 organizes the subset of the results using the familiarity levels and the familiarity level request. Then, method 800 ends.

The foregoing description of the preferred embodiments of the present invention is by way of example only, and other variations and modifications of the above-described embodiments and methods are possible in light of the foregoing teaching. Although the network sites are being described as separate and distinct sites, one skilled in the art will recognize that these sites may be a part of an integral site, may each include portions of multiple sites, or may include combinations of single and multiple sites. The various embodiments set forth herein may be implemented utilizing hardware, software, or any desired combination thereof. For that matter, any type of logic may be utilized which is capable of implementing the various functionality set forth herein. Components may be implemented using a programmed general purpose digital computer, using application specific integrated circuits, or using a network of interconnected conventional components and circuits. Connections may be wired, wireless, modem, etc. The embodiments described herein are not intended to be exhaustive or limiting. The present invention is limited only by the following claims. 

1. A method, comprising: receiving a document; conducting a stopwords analysis of stopwords in the document, wherein conducting the stopwords analysis includes conducting stopwords analysis of introductory level stopwords suggesting introductory content and advanced level stopwords suggesting advanced content; conducting a reading level analysis of the document; conducting a feature analysis of the document; generating a stopwords score based on the stopwords analysis; and generating a document familiarity level based on the stopwords score, the reading level analysis, and the feature analysis, wherein generating the document familiarity level includes implementing a familiarity level classification function that incorporates weighting coefficients for each of the stopwords analysis, reading level analysis and the feature analysis.
 2. The method of claim 1, wherein the document includes a website.
 3. The method of claim 1, wherein the document is received at index time.
 4. The method of claim 1, wherein the document is one of several documents returned in response to a search query.
 5. The method of claim 1, further comprising generating a stopwords score based on the stopwords analysis, the stopwords score including a sliding scale value.
 6. The method of claim 1, further comprising generating a stopwords score based on the stopwords analysis, the stopwords score including a descriptor from a set of descriptors.
 7. The method of claim 1, further comprising conducting a feature analysis of the document, and wherein the generating the document familiarity level is also based on the feature analysis.
 8. A non-transitory computer readable medium comprising: a stopwords engine configured to conduct a stopwords analysis of stopwords in a document and to generate a stopwords score based on the stopwords analysis, the stopwords score including a binary value, wherein the stopwords engine is configured to conduct stopwords analysis of introductory level stopwords suggesting introductory content and advanced level stopwords suggesting advanced content; a reading level engine configured to conduct a reading level analysis of the document; a document features engine configured to conduct a feature analysis of the document; and a familiarity level classifier module configured to generate a document familiarity level based on the stopwords score, the reading level analysis, and the feature analysis, the familiarity level classifier module is further configured to implement a familiarity level classification function that incorporates weighting coefficients for each of the stopwords analysis, reading level analysis and the feature analysis.
 9. The classifier of claim 8, wherein the document includes a website.
 10. The classifier of claim 8, wherein the classifier is in an indexing module.
 11. The classifier of claim 8, wherein the classifier is in a search engine.
 12. The classifier of claim 8, wherein the classifier is in a user computer.
 13. The classifier of claim 8, wherein the stopwords engine is configured to generate a stopwords score based on the stopwords analysis, the stopwords score including a sliding scale value.
 14. The classifier of claim 8, wherein the stopwords engine is configured to generate a stopwords score based on the stopwords analysis, the stopwords score including a descriptor from a set of descriptors.
 15. The classifier of claim 8, further comprising a document feature engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the document familiarity level also based on the feature analysis.
 16. A system, comprising: means for receiving a document; means for conducting a stopwords analysis of stopwords in the document, including stopwords analysis of introductory level stopwords suggesting introductory content and advanced level stopwords suggesting advanced content; means for conducting a reading level analysis of the document; means for conducting a feature analysis of the document; means for generating a stopwords score based on the stopwords analysis; and means for generating a document familiarity level based on the stopwords score, the reading level analysis, and the feature analysis, wherein generating the document familiarity level includes implementing a familiarity level classification function that incorporates weighting coefficients for each of the stopwords analysis, reading level analysis and the feature analysis. 